PDFFlow: parton distribution functions on GPU
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Abstract (arXiv)
We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators. PDFs are essential for the calculation of particle physics observables through Monte Carlo simulation techniques. The evaluation of a generic set of PDFs for quarks and gluon at a given momentum fraction and energy scale requires the implementation of interpolation algorithms as introduced for the first time by the LHAPDF project. PDFFlow extends and implements these interpolation algorithms using Google's TensorFlow library providing the capabilities to perform PDF evaluations taking fully advantage of multi-threading CPU and GPU setups. We benchmark the performance of this library on multiple scenarios relevant for the particle physics community.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Sep 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1.
5 papers.
- 2021A Living Review of Machine Learning for Particle Physicscited by 78
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- 2023Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physicscited by 6
- 2023Differentiable Vertex Fitting for Jet Flavour Taggingcited by 3
- 2024Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physicscited by 0
- 2024Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics25 Nov 2024
- 2023Differentiable Vertex Fitting for Jet Flavour Tagging19 Oct 2023
- 2023Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics31 Aug 2023
- 2022Differentiable Matrix Elements with MadJax28 Feb 2022
- 2021A Living Review of Machine Learning for Particle Physics2 Feb 2021